The x-ray beam in most computer tomography (CT) scanners is generally polychromatic. Yet, most of the currently utilized CT scanners generate images based upon data according to the energy integration nature of the detectors. These conventional detectors are called energy integrating detectors for acquiring energy integration X-ray data that cannot provide spectral information. On the other hand, photon counting detectors are configured to acquire the spectral nature of the x-ray source rather than the energy integration nature in the acquired data. To obtain the spectral nature of the transmitted X-ray, the photo counting detector splits the x-ray beam into its component energies or spectrum bins and counts a number of photons in each of the bins. The use of the spectral nature of the x-ray source in CT is often referred to as spectral CT. Since spectral CT involves the detection of transmitted X-ray at two or more energy levels, spectral CT generally includes dual-energy CT by definition.
Spectral CT is advantageous over conventional CT in certain aspects. Spectral CT offers the additional clinical information inherent in the full spectrum of an x-ray beam. For example, spectral CT enhances in discriminating tissues, differentiating between materials such as tissues containing calcium and iodine or enhancing the detection of smaller vessels. Among other advantages, spectral CT is also expected to reduce beam hardening artifacts. Spectral CT is expected to increase accuracy in CT numbers independent of scanners.
Prior art attempts for spectral CT unfortunately involve tradeoffs while trying to solve issues such as beam hardening, temporal resolution, noise balance, and inadequate energy separation. For example, dual source solutions are good for noise balance and energy separation but are not so good in some clinical applications for correcting beam hardening and improving temporal resolution. Fast kV-switching has the potential for good beam hardening correction and good temporal resolution although the noise balance might require a tradeoff with temporal resolution and inadequate energy separation might affect the precision of the reconstructed spectral images. Nonetheless, when utilized in the right clinical situations, prior art solutions can successfully improve diagnosis. On the other hand, spectral imaging with photon counting detectors has the potential for solving all four issues without tradeoffs as well as more advanced spectral techniques such as precise material characterization through k-edge imaging.
Prior art has also attempted to replace the conventional integrating detectors by the photon counting detectors in implementing spectral CT. In general, photon counting detectors are costly and have performance constraints under high flux x-rays. Although at least one experimental spectral CT system has been reported, the costs of high-rate photon counting detectors are prohibitive for a full-scale implementation. Despite some advancement in the photon counting detector technology, the currently available photon counting detectors still require solutions to implementation issues such as polarization due to space charge build-up, pile-up effects, scatter effects, spatial resolution, temporal resolution and dose efficiency.
Spectral CT is currently limited to dual energy approaches such as dual source CT, dual layer detector CT and fast-kV switching CT. In this regard, true spectral information beyond dual energy is not advantageously utilized in general purpose clinical CT. On the other hand, a true spectral CT system appears to face the above described issues related to the energy differentiating photon counting detectors.
For the above reasons, it is still desired to invent CT systems and methods of improving the use of spectral data as acquired by the photon counting detectors possibly in combination with energy integrated data as acquired by the energy integrating detectors.